The integrated circuit fabrication industry must continually produce faster and smaller devices to satisfy the expectations of the electronics industry and consumers. As the number of devices within an integrated circuit has skyrocketed, so has potential for failure of the integrated circuit increased, as a defect in any one of those millions of devices can potentially cripple the entire integrated circuit. Thus, inspections of various types, such as optical inspections, have become very important to the integrated circuit fabrication industry.
For example, integrated circuits of the type formed on monolithic semiconducting wafers, such as those formed of group IV materials such as silicon or germanium, or group III-V materials such as gallium arsenide, or combinations of such materials, are typically inspected at multiple points during the fabrication process, to ensure that material, processing, or handling problems have not created defects in the integrated circuits being formed.
However, as important as these inspections are, there are other inspections that can be even more important. For example, the patterned layers of which integrated circuits are formed are typically created with a photolithographic process, where the pattern for each layer is transferred to the wafer from a mask of some type, which is often in the form of a reticle. The reticle contains an image of the pattern that is to be transferred to the wafer. Typically, the pattern from the reticle is transferred to the wafer by optically stepping or otherwise scanning the reticle across the surface of the wafer, and exposing the pattern from the reticle onto the wafer in each of those stepped locations.
Because a single image from a reticle is used over and over again on a single wafer—perhaps hundreds or thousands of times, and is then used on many different wafers—perhaps hundreds of thousands of times, it is extremely important that the image on the reticle be as close to perfect as possible, or at the very least only have defects that are well within design parameters. Thus, the inspection of reticles is typically conducted with the very highest degree of care.
Because of the degree of care that is required, an operator is typically trained to review and classify on a one by one basis each defect that is detected by the reticle inspection system. Obviously, such a manual method of review and classification is extremely expensive and time consuming, and can easily take longer than the inspection process itself. Further, the differences in training and ability from one operator to another, and the problems associated with operator distraction or fatigue tend to introduce errors and inconsistencies into the classification process. Thus, manual classification of the defect data gathered from optical reticle inspections is somewhat unsatisfactory.
Unfortunately, automated reticle defect classification has proven to be very difficult to implement, and comes with problems of its own. For example, statistical or rule based methods of automatic defect classification can only be used in some applications, and even then these methods are generally limited by the metrics used to characterize the defect and environment and by the ability of those metrics to distinguish important differences between defects. Thus, prior art automated reticle defect classification has generally not produced adequate results, in that it tends to misclassify the defects that have been detected.
What is needed, therefore, is a system for the classification of optically detected reticle or other substrate defects that reduces or eliminates the problems described above, or other problems.